Prey choice, provisioning behaviour, and effects of early nutrition on nestling phenotype of titmice
Bibliographic record
Abstract
It is generally assumed that blue and great tits (Cyanistes caeruleus, Parus major) compete for the same type of food (Lepidoptera larvae) during the breeding season and that the former have some advantage because they are usually earlier and can exploit small caterpillars that are often more abundant and are not consumed by great tits. However, it is not clear whether, when confronted with similar circumstances (e.g., nestling demand), blue and great tits show similar preferences for a particular caterpillar type. In this 2-y study, we compare the diet of both tit species in detail by controlling for hatching date and brood size. We also examine how the contribution of caterpillars and spiders to the diet is related to nestling development. A positive relationship was found between the percentage of spiders in the diet and nestling tarsus length in both species, reinforcing the idea that neonatal nutrition could have a strong influence on nestling phenotype (e.g., offspring size). Such a correlation may arise because spiders contain a high level of taurine, an essential nutrient in the early development of young, and/or this prey type contains more calcium than other food items, which may affect the rate of nestling bone mineralization. Blue tits fed their young double the number of tortricid larvae in comparison with great tits, whereas the latter showed a clear preference for noctuids and exploited, with a low frequency, a type of larvae not consumed by blue tits, hairy caterpillars (Lasiocampidae). Our results point to resource partitioning by these species in this forest ecosystem and contribute to a better understanding of feeding ecology of titmice, which is particularly timely in a global warming context.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".